Machine Learning Skills you will learn

  • Supervised and unsupervised learning
  • Time series modeling
  • Linear and logistic regression
  • Kernel SVM
  • KMeans clustering
  • Naive Bayes
  • Decision tree
  • Random forest classifiers
  • Boosting and Bagging techniques
  • Deep Learning fundamentals

Who should learn this free Machine Learning course?

  • Analytics Managers
  • Business Analysts
  • Information Architects
  • Developers

What you will learn in this free Machine Learning course?

  • Machine Learning

    • Lesson 01 - Course Introduction

      04:34
      • 1.01 Course Introduction
        02:39
      • 1.02 What You Will Learn
        01:55
    • Lesson 02 - Introduction to Machine Learning

      14:30
      • 2.01 Introduction
        00:51
      • 2.02 What Is Machine Learning?
        02:49
      • 2.03 Types of Machine Learning
        02:54
      • 2.04 Machine Learning Pipeline and MLOP's
        03:35
      • 2.05 Introduction to Python Packages Used in Machine Learning
        03:29
      • 2.06 Recap
        00:52
    • Lesson 03 - Supervised Learning

      26:34
      • 3.01 Introduction
        00:41
      • 3.02 Supervised Learning
        02:34
      • 3.03 Applications of Supervised Learning
        03:11
      • 3.04 Preparing and Shaping Data
        06:50
      • 3.05 What is overfitting and underfitting?
        02:23
      • 3.06 Detecting and Preventing Overfitting and Underfitting
        07:36
      • 3.07 Regularization
        02:38
      • 3.08 Recap
        00:41
    • Lesson 04 - Regression and Applications

      01:09:22
      • 4.01 Introduction
        01:14
      • 4.02 What is Regression?
        01:34
      • 4.03 Regression Types: Introduction
        02:45
      • 4.04 Linear Regression
        02:48
      • 4.05 Working with Linear Regression
        13:59
      • 4.06 Critical Assumptions for Linear Regression
        01:31
      • 4.07 Logistic Regression
        02:33
      • 4.08 Data Exploration Using SMOTE
        12:56
      • 4.09 Over Sampling Using SMOTE
        01:48
      • 4.10 Polynomial Regression
        02:41
      • 4.11 Data Preparation Model Building and Performance Evaluation: Part A
        04:53
      • 4.12 Ridge Regression
        01:57
      • 4.13 Data Preparation Model Building: Part B
        06:25
      • 4.14 LASSO Regression
        02:30
      • 4.15 Data Preparation Model Building: Part C
        06:13
      • 4.16 Recap
        00:55
      • 4.17 Spotlight
        02:40
    • Lesson 05 - Classification and Applications

      01:05:11
      • 5.01 Introduction
        01:03
      • 5.02 What are Classification Algorithms?
        02:09
      • 5.03 Types of Classification
        03:29
      • 5.04 Types and selection of performance parameters
        04:58
      • 5.05 Naive Bayes
        02:56
      • 5.06 Applying Naive Bayes Classifier
        03:27
      • 5.07 Stochastic Gradient Descent
        03:25
      • 5.08 Applying Stochastic Gradient Descent
        05:02
      • 5.09 K Nearest Neighbours
        02:41
      • 5.10 Applying K Nearest Neighbours
        05:28
      • 5.11 Decision Tree
        02:42
      • 5.12 Applying Decision Tree
        04:27
      • 5.13 Random Forest
        01:59
      • 5.14 Applying Random Forest
        03:22
      • 5.15 Boruta Explained
        01:15
      • 5.16 Automatic Feature Selection with Boruta
        06:43
      • 5.17 Support Vector Machine
        02:27
      • 5.18 Applying Support Vector Machine
        05:34
      • 5.19 Cohens Kappa Measure
        01:22
      • 5.20 Recap
        00:42
    • Lesson 06 - Unsupervised Algorithms

      01:15:00
      • 6.01 Introduction
        00:53
      • 6.02 What are Unsupervised Algorithms?
        02:51
      • 6.03 Types of Unsupervised Algorithms Clustering and Associative
        02:15
      • 6.04 When to Use Unsupervised Algorithms?
        01:22
      • 6.05 Visualizing Outputs
        06:14
      • 6.06 Performance Parameters
        02:55
      • 6.07 Clustering Types
        00:56
      • 6.08 Hierarchical Clustering
        03:32
      • 6.09 Applying Hierarchical Clustering
        03:22
      • 6.10 K means Clustering: Part 1
        02:30
      • 6.11 K means Clustering: Part 2
        01:54
      • 6.12 Applying K Means Clustering
        03:37
      • 6.13 KNN-K Nearest Neighbors
        03:41
      • 6.14 Outlier Detection
        01:47
      • 6.15 Outlier Detection Algorithms in PyOD
        02:49
      • 6.16 Demo: K NN for Anomaly Detection
        02:37
      • 6.17 Principal Component Analysis
        04:15
      • 6.18 Applying Principal Component Analysis: PCA
        04:21
      • 6.19 Correspondence Analysis Multiple correspondence analysis: MCA
        03:16
      • 6.20 Singular Value Decomposition
        02:06
      • 6.21 Applying Singular Value Decomposition
        04:14
      • 6.22 Independent Component Analysis
        02:26
      • 6.23 Applying Independent Component Analysis
        01:54
      • 6.24 BIRCH
        02:33
      • 6.25 Applying BIRCH
        02:15
      • 6.26 Recap
        01:05
      • 6.27 Spotlight
        03:20
    • Lesson 07 - Ensemble Learning

      59:58
      • 7.01 Introduction
        00:54
      • 7.02 What is Ensemble Learning?
        01:46
      • 7.03 Categories in Ensemble Learning
        02:47
      • 7.04 Sequential Ensemble Technique
        02:50
      • 7.05 Parallel Ensemble Technique
        02:10
      • 7.06 Types of Ensemble Methods
        01:56
      • 7.07 Bagging
        03:01
      • 7.08 Demo: Bagging
        02:53
      • 7.09 Boosting
        02:14
      • 7.10 Demo: Boosting
        03:29
      • 7.11 Stacking
        02:56
      • 7.12 Demo: Stacking
        03:44
      • 7.13 Reducing Errors with Ensembles
        05:27
      • 7.14 Applying Averaging and Max Voting
        06:33
      • 7.15 Hello World Tensorflow
        02:38
      • 7.16 Hands on with TensorFlow: Part A
        05:09
      • 7.17 Keras
        02:49
      • 7.18 Hands on with TensorFlow: Part B
        05:57
      • 7.19 Recap
        00:45
    • Lesson 08 - Recommender System

      58:24
      • 8.01 Introduction
        01:00
      • 8.02 How do recommendation engines work
        02:45
      • 8.03 Recommendation Engine: Use Cases
        01:44
      • 8.04 Examples of Recommender System and Their Designs
        02:55
      • 8.05 Leveraging PyTorch to Build a Recommendation Engine
        02:23
      • 8.06 Collaborative Filtering and Memory Based Modeling
        06:31
      • 8.07 Item Based Collaborative Filtering
        07:02
      • 8.08 User Based Collaborative Filtering
        13:05
      • 8.09 Model Based Collaborative Filtering
        04:09
      • 8.10 Dimensionality Reduction and Matrix Factorization
        04:51
      • 8.11 Accuracy Matrices in ML
        08:06
      • 8.12 Recap
        00:52
      • 8.13 Spotlight
        03:01

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Learn the Basics of Machine Learning

Why you should learn Machine Learning?

$8.81 billion

Expected machine learning market growth by 2022

44.1% growth

In the adoption of machine learning in organizations

Career Opportunities

About the Course

This free machine learning course is designed to provide you with a solid foundation in machine learning, one of the most exciting and rapidly growing fields in technology and data science. Whether you're a beginner or a machine learning professional looking to refresh your knowledge, this course is just the right one for you!

Topics Covered:

Supervised and Unsupervised Learning: We dive into the two fundamental categories of machine

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FAQs

  • Is there a cost associated with this free Machine Learning course?

    No, this course is completely free, and you will receive a certificate upon completion.

  • What are the prerequisites to learn this free course on Machine Learning?

    There are no strict prerequisites. Basic programming knowledge and a passion for learning are helpful.

  • When can I expect to receive my certificate?

    You'll receive your certificate upon successfully completing the course.

  • What is the duration of my access to the course?

    Your access to the course is unlimited, so you can learn at your own pace.

  • How difficult is this free Machine Learning course?

    The course is designed to cater to learners of all levels, from beginners to advanced. It provides a structured learning path to make it accessible to everyone.

  • Is ML easier than AI?

    Machine learning is a subset of artificial intelligence (AI). AI encompasses a broader range of topics, while machine learning focuses on algorithms that enable computers to learn from data.

  • What is the salary of a Machine Learning engineer?

    Salaries can vary widely based on location and experience. On average, machine learning engineers earn competitive salaries due to the high demand for their skills.

  • What is the future scope of machine learning?

    Machine learning is a rapidly evolving field with a promising future. It's being applied across industries like healthcare, finance, and technology, making it a key driver of innovation and job opportunities.

Learner Review

  • M Ehsani

    M Ehsani

    Thanks to Simplilearn for providing such an insightful course on Machine Learning. Looking forward to applying my learnings in a real-world project.

  • Jyoti Dange

    Jyoti Dange

    The course was really good. I am thorough with the fundamentals of Machine Learning and I have recommended this course to my friends.

  • Rajeev Gaur

    Rajeev Gaur

    The course gave me a lot of exposure to the practical side of Machine Learning projects. It was an awesome experience.

  • Daren Lee

    Daren Lee

    The course material covered concepts with clarity through real-life examples.

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  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.